Phase Synchronization Analysis Research Articles

Sleep stages and brain oscillations: An in-depth analysis using quantitative and mathematical models

This study explores the intricate relationship between sleep stages and brain oscillations, utilizing quantitative EEG analysis and mathematical models. We examine the distinct EEG characteristics of light sleep, deep sleep, and REM sleep, focusing on power spectral density (PSD), coherence, and phase synchronization analyses. The Ising model and Kuramoto model provide frameworks for understanding the synchronization dynamics and phase relationships of neuronal populations across sleep stages. Our findings highlight significant variations in oscillatory activity, coherence, and phase synchronization, offering insights into the neural mechanisms underlying sleep regulation. Additionally, we discuss the implications of these findings for sleep disorders such as insomnia, sleep apnea, and narcolepsy, demonstrating how mathematical models can predict therapeutic outcomes and guide targeted interventions. This comprehensive analysis contributes to the understanding of sleep architecture and the development of novel treatments for sleep disorders.

Decreased Beta Power and OFC-STN Phase Synchronization during Reactive Stopping in Freely Behaving Rats.

During natural behavior, an action often needs to be suddenly stopped in response to an unexpected sensory input-referred to as reactive stopping. Reactive stopping has been mostly investigated in humans, which led to hypotheses about the involvement of different brain structures, in particular the hyperdirect pathway. Here, we directly investigate the contribution and interaction of two key regions of the hyperdirect pathway, the orbitofrontal cortex (OFC) and subthalamic nucleus (STN), using dual-area, multielectrode recordings in male rats performing a stop-signal task. In this task, rats have to initiate movement to a go-signal, and occasionally stop their movement to the go-signal side after a stop-signal, presented at various stop-signal delays. Both the OFC and STN show near-simultaneous field potential reductions in the beta frequency range (12-30 Hz) compared with the period preceding the go-signal and the movement period. These transient reductions (∼200 ms) only happen during reactive stopping, which is when the stop-signal was received after action initiation, and are well timed after stop-signal onset and before the estimated time of stopping. Phase synchronization analysis also showed a transient attenuation of synchronization between the OFC and STN in the beta range during reactive stopping. The present results provide the first direct quantification of local neural oscillatory activity in the OFC and STN and interareal synchronization specifically timed during reactive stopping.

Wireless Power Transfer System Model Reduction with Split Frequency Matching

Reduced-order dynamic models of wireless power transfer (WPT) systems are desired to simplify the analysis and design of power control, phase synchronization, and maximum efficiency tracking. The reduced-order dynamic phasor model is a good choice because of its straightforward physical meaning and concise mathematical formula. However, the model relies on the assumption of loose coupling and loses accuracy when the coupling becomes stronger. In this paper, a model reduction method with split frequency matching is proposed to improve model accuracy under relatively strong coupling conditions, which is suitable for most short-distance WPT applications, such as wireless electrical vehicle charging. Split frequency matching is achieved through a pair of conjugate equivalent mutual inductances, which are derived from the asymmetry characteristics of the full-order dynamic phasor model in the positive and negative frequency domains. The proposed model retains the advantages of the existing model while significantly improving the accuracy under strong coupling conditions. Its characteristics are verified by comparing the experimental results and model predictions under both large step changes and small-signal perturbations.

Predictive maintenance in electrical power systems: Thermography and statistical methods for phase synchronization analysis in disconnected substations

Phase synchronization can aid in understanding the dynamics of underlying systems. In an electric substation, disconnect, failures mostly occur due to overheating. In this work using the Hilbert transform to assess the phase synchrony of a disconnect temperature and its relationship with meteorological variables and disconnect current can be helpful in understanding temperature influences and composition. Statistical methodologies, including entropy analysis, correlation, Kalman filter, Fast Fourier Transform, Augmented Dickey–Fuller test, and Kwiatkowski–Phillips–Schmidt–Shin test, were employed. One of the differentials of this study is the use of the median filter, which was able to access the phase synchrony of the variables with disconnect temperature. An important result is that current is not the most correlated variable with disconnect temperature, instead, air temperature exhibits the highest correlation, and the highest phase synchronization is only obtained when disconnect current and air temperatures are combined. This study contributes to the advancement of power substation operations, advocating for continuous monitoring and analysis to ensure a reliable electricity supply obtaining better predictive maintenance.

Association of brain functional connectivity with neurodevelopmental outcomes in healthy full-term newborns

ObjectiveTo study the association between neurodevelopmental outcomes and functional brain connectivity (FBC) in healthy term infants. MethodsThis is a retrospective study of prospectively collected High-density electroencephalography (HD-EEG) from newborns within 72 hours from birth. Developmental assessments were performed at two years of age using the Bayley Scales of Infant Development-III (BSID-III) measuring cognitive, language, motor, and socio-emotional scores. The FBC was calculated using phase synchronization analysis of source signals in delta, theta, alpha, beta, and gamma frequency bands and its association with neurodevelopmental score was assessed with stepwise regression. Results47/163 had both HD-EEG and BSID-III scores. The FBC of frontal region was associated with cognitive score in the theta band (corrected p, regression coefficients range: p < 0.01, 1.66–1.735). Language scores were significantly associated with connectivity in all frequency bands, predominantly in the left hemisphere (p < 0.01, −2.74–2.40). The FBC of frontal and occipital brain regions of both hemispheres was related to motor score and socio-emotional development in theta, alpha, and gamma frequency bands (p < 0.01, −2.16–2.97). ConclusionsFunctional connectivity of higher-order processing is already present at term age. SignificanceThe FBC might be used to guide interventions for optimizing subsequent neurodevelopment even in low-risk newborns.

EEG decoding for musical emotion with functional connectivity features

Emotion refers to the subjective emotional experience that humans generate in specific situations, typically accompanied by physiological and psychological changes. In the field of emotions, multi-channel EEG emotional features can better reflect the collaborative mechanisms across multiple brain regions. Therefore, we propose a novel feature called asPLV (averaged sub-frequency phase locking value) based on the Morlet transform method to construct functional network edges. The proposed feature encompasses a comprehensive analysis of phase synchronization across sub-frequency bands spanning a wide range of frequencies and has the potential to reduce fluctuations arising from reliance on a single frequency band. We designed a music-evoked emotion experiment aimed at inducing corresponding emotions in participants while simultaneously recording their electroencephalogram (EEG) signals and extracted our proposed asPLV feature for classification. The results show that the proposed feature displays superior classification performance and generalization compared to other state-of-the-art methods. The proposed method is not only effective in successfully distinguishing between different emotions but also introduces a novel brain network metric to elucidate the collaboration and information exchange among emotion-related brain regions. Moreover, the asPLV feature could provide new insights for the development of an emotional brain-computer interface (BCI).

Fronto-parietal theta high-definition transcranial alternating current stimulation may modulate working memory under postural control conditions in young healthy adults.

This study aimed to investigate the immediate effects of fronto-parietal θ HD-tACS on a dual task of working memory-postural control. In this within-subject cross-over pilot study, we assessed the effects of 20 min of 6 Hz-tACS targeting both the left dorsolateral prefrontal cortex (lDLPFC) and posterior parietal cortex (PPC) in 20 healthy adults (age: 21.6 ± 1.3 years). During each session, single- and dual-task behavioral tests (working memory single-task, static tandem standing, and a dual-task of working memory-postural control) and closed-eye resting-state EEG were assessed before and immediately after stimulation. Within the tACS group, we found a 5.3% significant decrease in working memory response time under the dual-task following tACS (t = -3.157, p = 0.005, Cohen's d = 0.742); phase synchronization analysis revealed a significant increase in the phase locking value (PLV) of θ band between F3 and P3 after tACS (p = 0.010, Cohen's d = 0.637). Correlation analyses revealed a significant correlation between increased rs-EEG θ power in the F3 and P3 channels and faster reaction time (r = -0.515, p = 0.02; r = -0.483, p = 0.031, respectively) in the dual-task working memory task after tACS. However, no differences were observed on either upright postural control performance or rs-EEG results (p-values <0.05). Fronto-parietal θ HD-tACS has the potential of being a neuromodulatory tool for improving working memory performance in dual-task situations, but its effect on the modulation of concurrently performed postural control tasks requires further investigation.

Frequency-specific phase synchronization analysis of a stall-induced aeroelastic system undergoing 2:1 internal resonance in a low-speed wind tunnel

Frequency-specific phase synchronization analysis of a stall-induced aeroelastic system undergoing 2:1 internal resonance in a low-speed wind tunnel

Intra- and interbrain synchrony and hyperbrain network dynamics of a guitarist quartet and its audience during a concert.

Playing music in a concert represents a multilevel interaction between musicians and the audience, where interbrain synchronization might play an essential role. Here, we simultaneously recorded electroencephalographs (EEGs) from the brains of eight people during a concert: a quartet of professional guitarists and four participants in the audience. Using phase synchronization analyses between EEG signals within and between brains, we constructed hyperbrain networks, comprising synchronized brain activity across the eight brains, and analyzed them using a graph-theoretical approach. We found that strengths within and between brains in the delta band were higher in the quartet than in the public. Within-brain strengths were higher and between-brain strengths were lower in the music than in the applause condition, both particularly in the quartet group. These changes in coupling strength were accompanied by corresponding changes in the hyperbrain network topology, which were also frequency-specific. Moreover, the network topology and the dynamical structure of guitar sounds showed specific guitar-brain, guitar-guitar, and brain-brain directional associations, indicating multilevel dynamics with upward and downward causation. Finally, the hyperbrain networks exhibit modular structures that were more stable during music performance than during applause. Our findings illustrate complex hyperbrain network interactions in a quartet and its audience during a concert.

Neural responses to biological motion distinguish autistic and schizotypal traits.

Difficulties in social interactions characterize both autism and schizophrenia and are correlated in the neurotypical population. It is unknown whether this represents a shared etiology or superficial phenotypic overlap. Both conditions exhibit atypical neural activity in response to the perception of social stimuli and decreased neural synchronization between individuals. This study investigated if neural activity and neural synchronization associated with biological motion perception are differentially associated with autistic and schizotypal traits in the neurotypical population. Participants viewed naturalistic social interactions while hemodynamic brain activity was measured with fMRI, which was modeled against a continuous measure of the extent of biological motion. General linear model analysis revealed that biological motion perception was associated with neural activity across the action observation network. However, intersubject phase synchronization analysis revealed neural activity to be synchronized between individuals in occipital and parietal areas but desynchronized in temporal and frontal regions. Autistic traits were associated with decreased neural activity (precuneus and middle cingulate gyrus), and schizotypal traits were associated with decreased neural synchronization (middle and inferior frontal gyri). Biological motion perception elicits divergent patterns of neural activity and synchronization, which dissociate autistic and schizotypal traits in the general population, suggesting that they originate from different neural mechanisms.

Cardiorespiratory coupling in mechanically ventilated patients studied via synchrogram analysis.

Respiration and cardiac activity are strictly interconnected with reciprocal influences. They act as weakly coupled oscillators showing varying degrees of phase synchronization and their interactions are affected by mechanical ventilation. The study aims at differentiating the impact of three ventilatory modes on the cardiorespiratory phase coupling in critically ill patients. The coupling between respiration and heartbeat was studied through cardiorespiratory phase synchronization analysis carried out via synchrogram during pressure control ventilation (PCV), pressure support ventilation (PSV), and neurally adjusted ventilatory assist (NAVA) in critically ill patients. Twenty patients were studied under all the three ventilatory modes. Cardiorespiratory phase synchronization changed significantly across ventilatory modes. The highest synchronization degree was found during PCV session, while the lowest one with NAVA. The percentage of all epochs featuring synchronization regardless of the phase locking ratio was higher with PCV (median: 33.9%, first-third quartile: 21.3-39.3) than PSV (median: 15.7%; first-third quartile: 10.9-27.8) and NAVA (median: 3.7%; first-third quartile: 3.3-19.2). PCV induces a significant amount of cardiorespiratory phase synchronization in critically ill mechanically ventilated patients. Synchronization induced by patient-driven ventilatory modes was weaker, reaching the minimum with NAVA. Findings can be explained as a result of the more regular and powerful solicitation of the cardiorespiratory system induced by PCV. The degree of phase synchronization between cardiac and respiratory activities in mechanically ventilated humans depends on the ventilatory mode.

Ongoing first-hand pain facilitates somatosensory resonance but inhibits affective sharing in empathy for pain

Alterations of empathy for others’ pain among patients with chronic pain remained inconsistent. Here, applying a capsaicin-based ongoing pain model on healthy participants, this study investigated how ongoing first-hand pain influences empathic reactions to vicarious pain stimuli. Healthy participants were randomly treated with topical capsaicin cream (capsaicin group) or hand cream (control group) on the left forearm. Video clips showing limbs in painful and non-painful situations were used to induce empathic responses. The capsaicin group showed greater empathic neural responses in the right primary somatosensory cortex (S1) than the control group but smaller responses in the left anterior insula (AI) accompanied with smaller empathic pain-intensity ratings. Notably, the intensity of ongoing pain negatively correlated with empathy-related neural responses in the left AI. Inter-subject phase synchronization analysis was used to assess stimulus-dependent dynamic functional connectivity within or between brain regions engaged in pain empathy. The capsaicin group showed greater empathy-related neural synchronization within S1 and between S1 and AI, but less synchronization within AI and between AI and MCC. Behaviorally, the differential inter-subject pain-intensity rating alignment between painful and non-painful videos was more positive for the capsaicin group than for the control group, and this effect was partially mediated by the inter-subject neural synchronization between S1 and AI. These results suggest that ongoing first-hand pain facilitates neural activation and synchronization within brain regions associated with empathy-related somatosensory resonance at the cost of inhibiting activation and synchronization within brain regions engaged in empathy-related affective sharing.

Speech to noise ratio improvement induces nonlinear parietal phase synchrony in hearing aid users.

ObjectivesComprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on correlates of listening effort across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a phase synchrony analysis of electroencephalogram (EEG) signals.DesignThe EEG was recorded while 22 HI participants fitted with HAs performed a continuous speech in noise (SiN) task in the presence of background noise and a competing talker. The phase synchrony within eight regions of interest (ROIs) and four conventional EEG bands was computed by using a multivariate phase synchrony measure.ResultsThe results demonstrated that the activation of NR in HAs affects the EEG phase synchrony in the parietal ROI at low SNR differently than that at high SNR. The relationship between conditions of the listening task and phase synchrony in the parietal ROI was nonlinear.ConclusionWe showed that the activation of NR schemes in HAs can non-linearly reduce correlates of listening effort as estimated by EEG-based phase synchrony. We contend that investigation of the phase synchrony within ROIs can reflect the effects of HAs in HI individuals in ecological listening conditions.

Recurrence-Based Synchronization Analysis of Weakly Coupled Bursting Neurons under External ELF Fields.

We investigate the response characteristics of a two-dimensional neuron model exposed to an externally applied extremely low frequency (ELF) sinusoidal electric field and the synchronization of neurons weakly coupled with gap junction. We find, by numerical simulations, that neurons can exhibit different spiking patterns, which are well observed in the structure of the recurrence plot (RP). We further study the synchronization between weakly coupled neurons in chaotic regimes under the influence of a weak ELF electric field. In general, detecting the phases of chaotic spiky signals is not easy by using standard methods. Recurrence analysis provides a reliable tool for defining phases even for noncoherent regimes or spiky signals. Recurrence-based synchronization analysis reveals that, even in the range of weak coupling, phase synchronization of the coupled neurons occurs and, by adding an ELF electric field, this synchronization increases depending on the amplitude of the externally applied ELF electric field. We further suggest a novel measure for RP-based phase synchronization analysis, which better takes into account the probabilities of recurrences.

Reconstruction of Pulse Wave and Respiration From Wrist Accelerometer During Sleep.

Nocturnal recordings of heart rate and respiratory rate usually require several separate sensors or electrodes attached to different body parts - a disadvantage for at-home screening tests and for large cohort studies. In this paper, we demonstrate that a state-of-the-art accelerometer placed at subjects' wrists can be used to derive reliable signal reconstructions of heartbeat (pulse wave intervals) and respiration during sleep. Based on 226 full-night recordings, we evaluate the performance of our signal reconstruction methodology with respect to polysomnography. We use a phase synchronization analysis metrics that considers individual heartbeats or breaths. The quantitative comparison reveals that pulse-wave signal reconstructions are generally better than respiratory signal reconstructions. The best quality is achieved during deep sleep, followed by light sleep N2 and REM sleep. In addition, a suggested internal evaluation of multiple derived reconstructions can be used to identify time periods with highly reliable signals, particularly for pulse waves. Furthermore, we find that pulse-wave reconstructions are hardly affected by apnea and hypopnea events. During sleep, pulse wave and respiration signals can simultaneously be reconstructed from the same accelerometer recording at the wrist without the need for additional sensors. Reliability can be increased by internal evaluation if the reconstructed signals are not needed for the whole sleep duration. The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects' normal sleep environment.

Method for Cardiointervalogram Selection from a Photoplethysmogram Signal for Estimating the Total Percentage of Phase Synchronization of Contours of Autonomic Circulation Regulation

In this paper, we compare four methods of extracting the sequence of intervals between heartbeats from the photoplethysmogram signal to estimate the total percentage of phase synchronization of autonomic regulation circuits of blood circulation that are designed to operate in real time. In the analysis of the experimental ensemble of records of healthy subjects, the optimal parameters for the used approaches were selected. We compared cardiointervalograms obtained from photoplethysmogram signals with cardiointervalograms isolated from simultaneously recorded electrocardiogram signals during the analysis of phase synchronization between the phases of the extracted signals in the low-frequency range (0.04–0.15 Hz), which reflects the element activity of autonomic control of blood circulation. The operability of the approaches used in the analysis of the total percentage of phase synchronization of the autonomic regulation circuits of the cardiovascular system based on the photoplethysmogram signal for groups of healthy people and patients during COVID-19 was demonstrated.

Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain

Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.

Using a mathematical model of cardiovascular system for preparing surrogate data for testing methods of phase synchronization analysis

Using a mathematical model of cardiovascular system for preparing surrogate data for testing methods of phase synchronization analysis

Dynamic functional connectivity underlying temporal summation of pain in fibromyalgia

Compared to healthy controls (HC), fibromyalgia (FM) patients exhibit enhanced temporal summation of pain (TSP) – i.e. perception of increasingly greater pain in response to repetitive or sustained noxious stimuli. Previous neuroimaging studies have linked static functional connectivity (sFC) and stimulus-evoked brain fMRI response to enhanced TSP in FM patients. However, dynamics of brain organization in FM underlying ongoing changes in pain perception, such as that during TSP, is not known. The purpose of this study was to investigate the dynamic changes in functional brain connectivity (dFC) occurring during TSP in FM. We collected high temporal resolution (TR=1.25s) 6-minute resting-state (REST) and tonic leg cuff pressure pain (PAIN) accelerated fMRI brain scan from 84 FM patients and 38 HCs. Pain ratings were collected retrospectively for each 2-minute block of the PAIN scan, and TSP was calculated as last minus first pain rating. We used an instantaneous phase synchrony analysis approach to estimate dFC, followed by the application of a multi-slice community detection algorithm to reveal the dynamic community organization of the brain over time. We found that FM patients exhibit greater TSP than HCs (meanFM = 17.93, meanHC = 9.47, p = 7.58 × 10-4). In FM (and not HC), during PAIN versus REST, fMRI signal from the contralateral leg area of primary somatosensory cortex (S1leg) spent more time in the same community as salience network regions. However, both FM and HC groups showed decreased S1leg community enmeshment with other somatotopic areas of S1 during PAIN compared to during REST. Furthermore, longer and more consistent enmeshment of the sensorimotor and salience communities throughout the PAIN scan was associated with greater TSP in FM patients. Overall, this study corroborates our prior tonic cuff pain static connectivity results and reveals that dynamics in brain organization track changes in pain perception reflecting TSP in FM. NCCIH, NIH R61/R33-AT009306 (VN) and NIAMS, NIH R01-AR064367 (VN, RRE).

Response Inhibition Circuit Dysregulation in Substance Use Disorders Identified With Event-Related Potential-Derived Functional Connectivity

Dysregulated region-to-region activity in brain circuits that underlie successful response inhibition is common among substance use disorders (SUDs). Specific to response inhibition is the circuit (i.e., functional connectivity [FC]) between the anterior cingulate cortex (ACC) and bi-lateral dorsolateral prefrontal cortex (dlPFC), often measured with functional magnetic resonance imaging (fMRI). Inter-Channel Phase Synchrony (ICPS) analysis in the theta frequency range is a novel technique to identify FC from event-related potentials (ERPs).